Generation Forecasting for Small Run-of-the-River Hydroelectric Systems Using Statistical Learning Models

Other · Tue Jul 03 04:00:00 EDT 2018

OSTI ID:1630745

Song, Nam ^[1]

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1. Hydro Research Foundation

With an increasing interest in constructing new run-of-the-river (ROR) hydroelectric generation over the more traditional reservoir-based hydroelectric systems, there is an increasing operational challenge due to the volatility of stream flow. The Snohomish County Public Utilities District (SnoPUD) has recently invested in the construction and operation of 3 new run-of-the-river projects in Northwestern Washington along Calligan Creek, Hancock Creek, and Youngs Creek. In order to effectively plan generation dispatch, SnoPUD has expressed interest in the development of an accurate forecasting tool to predict the generation capacity for these ROR systems. The following research project aims to use statistical learning models, namely Hidden Markov Models (HMMs), to predict day-ahead generation capacities for the aforementioned ROR systems. These models are constructed using 12 years of historical stream flow data collected at the intake sites and precipitation data recorded at the National Oceanic and Atmospheric Administration (NOAA) Alpine Meadows station. Four methods of constructing the models are studied for their forecast accuracies, and are compared with the persistence model. Despite using only one set of observable variables, the HMMs are shown to have slight improvements in accuracy over the persistence model approach, which shows great optimism for future work.

• Research Organization: Hydro Research Foundation
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Water Power Technologies Office (EE-4WP)
• DOE Contract Number: EE0006506
• OSTI ID: 1630745
• Report Number(s): Final Report - EE0006506
• Country of Publication: United States
• Language: English

Related Subjects

13 HYDRO ENERGY